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Karunakaran KB, Amemori KI. Spatiotemporal expression patterns of anxiety disorder-associated genes. Transl Psychiatry 2023; 13:385. [PMID: 38092764 PMCID: PMC10719387 DOI: 10.1038/s41398-023-02693-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 11/25/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
Anxiety disorders (ADs) are the most common form of mental disorder that affects millions of individuals worldwide. Although physiological studies have revealed the neural circuits related to AD symptoms, how AD-associated genes are spatiotemporally expressed in the human brain still remains unclear. In this study, we integrated genome-wide association studies of four human AD subtypes-generalized anxiety disorder, social anxiety disorder, panic disorder, and obsessive-compulsive disorder-with spatial gene expression patterns. Our investigation uncovered a novel division among AD-associated genes, marked by significant and distinct expression enrichments in the cerebral nuclei, limbic, and midbrain regions. Each gene cluster was associated with specific anxiety-related behaviors, signaling pathways, region-specific gene networks, and cell types. Notably, we observed a significant negative correlation in the temporal expression patterns of these gene clusters during various developmental stages. Moreover, the specific brain regions enriched in each gene group aligned with neural circuits previously associated with negative decision-making and anxious temperament. These results suggest that the two distinct gene clusters may underlie separate neural systems involved in anxiety. As a result, our findings bridge the gap between genes and neural circuitry, shedding light on the mechanisms underlying AD-associated behaviors.
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Affiliation(s)
- Kalyani B Karunakaran
- Institute for the Advanced Study of Human Biology, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Ken-Ichi Amemori
- Institute for the Advanced Study of Human Biology, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan.
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Orellana R, Arancibia A, Badilla L, Acosta J, Arancibia G, Escar R, Ferrada G, Seeger M. Ecophysiological Features Shape the Distribution of Prophages and CRISPR in Sulfate Reducing Prokaryotes. Microorganisms 2021; 9:microorganisms9050931. [PMID: 33925267 PMCID: PMC8146710 DOI: 10.3390/microorganisms9050931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/18/2021] [Accepted: 04/23/2021] [Indexed: 12/28/2022] Open
Abstract
Sulfate reducing prokaryotes (SRP) are a phylogenetically and physiologically diverse group of microorganisms that use sulfate as an electron acceptor. SRP have long been recognized as key players of the carbon and sulfur cycles, and more recently, they have been identified to play a relevant role as part of syntrophic and symbiotic relations and the human microbiome. Despite their environmental relevance, there is a poor understanding about the prevalence of prophages and CRISPR arrays and how their distribution and dynamic affect the ecological role of SRP. We addressed this question by analyzing the results of a comprehensive survey of prophages and CRISPR in a total of 91 genomes of SRP with several genotypic, phenotypic, and physiological traits, including genome size, cell volume, minimum doubling time, cell wall, and habitat, among others. Our analysis discovered 81 prophages in 51 strains, representing the 56% of the total evaluated strains. Prophages are non-uniformly distributed across the SRP phylogeny, where prophage-rich lineages belonged to Desulfovibrionaceae and Peptococcaceae. Furthermore, our study found 160 CRISPR arrays in 71 SRP, which is more abundant and widely spread than previously expected. Although there is no correlation between presence and abundance of prophages and CRISPR arrays at the strain level, our analysis showed that there is a directly proportional relation between cellular volumes and number of prophages per cell. This result suggests that there is an additional selective pressure for strains with smaller cells to get rid of foreign DNA, such as prophages, but not CRISPR, due to less availability of cellular resources. Analysis of the prophage genes encoding viral structural proteins reported that 44% of SRP prophages are classified as Myoviridae, and comparative analysis showed high level of homology, but not synteny, among prophages belonging to the Family Desulfovibrionaceae. We further recovered viral-like particles and structures that resemble outer membrane vesicles from D. vulgaris str. Hildenborough. The results of this study improved the current understanding of dynamic interactions between prophages and CRISPR with their hosts in both cultured and hitherto-uncultured SRP strains, and how their distribution affects the microbial community dynamics in several sulfidogenic natural and engineered environments.
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Affiliation(s)
- Roberto Orellana
- Laboratorio de Biología Celular y Ecofisiología Microbiana, Facultad de Ciencias Naturales y Exactas, Universidad de Playa Ancha, Leopoldo Carvallo 270, Valparaíso 2360001, Chile; (A.A.); (L.B.)
- Correspondence:
| | - Alejandra Arancibia
- Laboratorio de Biología Celular y Ecofisiología Microbiana, Facultad de Ciencias Naturales y Exactas, Universidad de Playa Ancha, Leopoldo Carvallo 270, Valparaíso 2360001, Chile; (A.A.); (L.B.)
| | - Leonardo Badilla
- Laboratorio de Biología Celular y Ecofisiología Microbiana, Facultad de Ciencias Naturales y Exactas, Universidad de Playa Ancha, Leopoldo Carvallo 270, Valparaíso 2360001, Chile; (A.A.); (L.B.)
| | - Jonathan Acosta
- Departamento de Estadística, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, Santiago 7820436, Chile;
| | - Gabriela Arancibia
- Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Departamento de Química & Centro de Biotecnología Daniel Alkalay-Lowitt, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso 2390123, Chile; (G.A.); (R.E.); (G.F.); (M.S.)
| | - Rodrigo Escar
- Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Departamento de Química & Centro de Biotecnología Daniel Alkalay-Lowitt, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso 2390123, Chile; (G.A.); (R.E.); (G.F.); (M.S.)
| | - Gustavo Ferrada
- Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Departamento de Química & Centro de Biotecnología Daniel Alkalay-Lowitt, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso 2390123, Chile; (G.A.); (R.E.); (G.F.); (M.S.)
| | - Michael Seeger
- Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Departamento de Química & Centro de Biotecnología Daniel Alkalay-Lowitt, Universidad Técnica Federico Santa María, Avenida España 1680, Valparaíso 2390123, Chile; (G.A.); (R.E.); (G.F.); (M.S.)
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Furuhata H, Araki K, Ogawa T. Event Surrogate from Clinical Pathway Completion to Daily Meal for Availability Extension Using Standard Electronic Medical Records: a Retrospective Cohort Study. J Med Syst 2021; 45:33. [PMID: 33547499 DOI: 10.1007/s10916-021-01714-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 01/20/2021] [Indexed: 11/24/2022]
Abstract
This study aimed to improve generalizability of our previous study that analyzed clinical pathway (CP) completion. Although our previous study demonstrated that CP completion can reduce the length of hospital stay, it is possible for few medical organizations to extract the implementation of treatment registered on CP from typical electronic medical records. Therefore, we have defined a prospective event for event substitution, called meal completion (MC), in which patients can take their meal daily. Data were collected from April 2013 to March 2018 from the electronic medical records of the University of Miyazaki Hospital. We used propensity score matching to extract records from 8033 patients. Patients were further divided into the MC and non-MC groups; 2577 patients in each group were available for data analysis. The numbers of patients with CP completion were 646 (28.1%) in the MC group and 411 (18.2%) in the non-MC group. The P value of the chi-square test was <0.001. According to this result, there was the causation from MC to increase in CP completion. Additionally, it was possible to consider the inclusion relationship in all treatments (universal set), treatments registered on CP (subset of all treatments), and meals (subset of treatments registered on CP). In conclusion, MC can substitute for CP completion because the demonstration is appropriate for the Prentice criterion, which is often used for the evaluation of a surrogate endpoint.
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Affiliation(s)
- Hiroki Furuhata
- Department of Hospital Institutional Research, University of Miyazaki Hospital, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan. .,Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan.
| | - Kenji Araki
- Department of Hospital Institutional Research, University of Miyazaki Hospital, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan
| | - Taisuke Ogawa
- Department of Hospital Institutional Research, University of Miyazaki Hospital, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan
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Burren OS, Reales G, Wong L, Bowes J, Lee JC, Barton A, Lyons PA, Smith KGC, Thomson W, Kirk PDW, Wallace C. Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases. Genome Med 2020; 12:106. [PMID: 33239102 PMCID: PMC7687775 DOI: 10.1186/s13073-020-00797-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/02/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging. METHODS Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension "basis" that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging. RESULTS We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations. CONCLUSIONS Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.
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Affiliation(s)
- Oliver S Burren
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Limy Wong
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - John Bowes
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - James C Lee
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Anne Barton
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Paul A Lyons
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Kenneth G C Smith
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Wendy Thomson
- National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Paul D W Kirk
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
- Cancer Research UK Cambridge Centre, Ovarian Cancer Programme, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Puddicombe Way, Cambridge, CB2 0AW, UK.
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
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Baraki F, Gebregergis Z, Belay Y, Berhe M, Teame G, Hassen M, Gebremedhin Z, Abadi A, Negash W, Atsbeha A, Araya G. Multivariate analysis for yield and yield-related traits of sesame ( Sesamum indicum L.) genotypes. Heliyon 2020; 6:e05295. [PMID: 33117902 PMCID: PMC7581916 DOI: 10.1016/j.heliyon.2020.e05295] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/15/2020] [Accepted: 10/14/2020] [Indexed: 11/23/2022] Open
Abstract
Sesame production under irrigation is limited in Ethiopia because of in availability of high yielding varieties, inadequate and inefficient irrigation schemes, and insignificant awareness of producers. This study, comprising 13 sesame genotypes, was conducted around Humera and Werer during 2018 and 2019 under irrigation. The design was randomized completely block design with three replications and the objectives were to develop high yielding genotypes and identify important agronomic traits. Multivariate statistical methods like Additive Main Effect and Multiplicative Interaction (AMMI) model, Principal Component Analysis, Cluster and factor analyses were used. The genotypes (6.22%), environments (42.62) and Genotype × Environment Interactions (25.09%) were statistically (p < 0.001) significant for the agronomic traits. The grain yield in each observation varied from 383 kg/ha to 2044 kg/ha and the grand mean yield was 820.19 kg/ha. The highest mean yield was recorded from G12 (948.6 kg/ha) followed by G4 (938.9 kg/ha) while the lowest was recorded from G8 (703.1 kg/ha). G1, G4, G12, G5, G8, G11 and G13 are identified as unstable genotypes while G2, G3, G6, and G9 are stable genotypes. The genotypes were grouped in to four clusters and cluster-II was characterized as the high yielding cluster and it was also associated with grain yield, pods per plant, branches per plant and thousand seed weight. Branches per plant, pods per plant and thousand seed weight may be most determinant and crucial in developing high yielding sesame varieties. This finding recommends that G4 and G6 are desirable genotypes and can be used for irrigation production.
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Affiliation(s)
- Fiseha Baraki
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Zenawi Gebregergis
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Yirga Belay
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Muez Berhe
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Goitom Teame
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Mohammed Hassen
- Ethiopian Institute of Agricultural Research, Wolkite Agricultural Research Center, Addis Ababa, Ethiopia
| | - Zerabruk Gebremedhin
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Assefa Abadi
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Weres Negash
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Alem Atsbeha
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
| | - Goitom Araya
- Tigray Agricultural Research Institute, Humera Agricultural Research Center, Tigray, Ethiopia
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McGuirl MR, Smith SP, Sandstede B, Ramachandran S. Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics. Genetics 2020; 215:511-529. [PMID: 32245788 PMCID: PMC7268989 DOI: 10.1534/genetics.120.303096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/31/2020] [Indexed: 12/31/2022] Open
Abstract
Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or "clusters," sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.
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Affiliation(s)
- Melissa R McGuirl
- Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912
| | - Samuel Pattillo Smith
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island 02912
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912
| | - Björn Sandstede
- Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912
- Data Science Initiative, Brown University, Providence, Rhode Island 02912
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island 02912
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912
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Dagan H, Flashner-Abramson E, Vasudevan S, Jubran MR, Cohen E, Kravchenko-Balasha N. Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures. Biomolecules 2020; 10:biom10040503. [PMID: 32225014 PMCID: PMC7226317 DOI: 10.3390/biom10040503] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/23/2020] [Accepted: 03/24/2020] [Indexed: 12/27/2022] Open
Abstract
Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.
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Affiliation(s)
- Hila Dagan
- The Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem 9190416, Israel;
| | - Efrat Flashner-Abramson
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Swetha Vasudevan
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Maria R. Jubran
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
| | - Ehud Cohen
- Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel—Canada, The Hebrew University School of Medicine, Jerusalem 9112102, Israel;
| | - Nataly Kravchenko-Balasha
- Department for Bio-Medical Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel; (E.F.-A.); (S.V.); (M.R.J.)
- Correspondence:
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8
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Islam S, Anand S, Hamid J, Thabane L, Beyene J. Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration. Stat Appl Genet Mol Biol 2017; 16:199-216. [PMID: 28727569 DOI: 10.1515/sagmb-2016-0066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Linear principal component analysis (PCA) is a widely used approach to reduce the dimension of gene or miRNA expression data sets. This method relies on the linearity assumption, which often fails to capture the patterns and relationships inherent in the data. Thus, a nonlinear approach such as kernel PCA might be optimal. We develop a copula-based simulation algorithm that takes into account the degree of dependence and nonlinearity observed in these data sets. Using this algorithm, we conduct an extensive simulation to compare the performance of linear and kernel principal component analysis methods towards data integration and death classification. We also compare these methods using a real data set with gene and miRNA expression of lung cancer patients. First few kernel principal components show poor performance compared to the linear principal components in this occasion. Reducing dimensions using linear PCA and a logistic regression model for classification seems to be adequate for this purpose. Integrating information from multiple data sets using either of these two approaches leads to an improved classification accuracy for the outcome.
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Pouladi N, Achour I, Li H, Berghout J, Kenost C, Gonzalez-Garay ML, Lussier YA. Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records. Yearb Med Inform 2016; 25:194-206. [PMID: 27830251 PMCID: PMC5171562 DOI: 10.15265/iy-2016-040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVES Disease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity. METHODS We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present. RESULTS Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of disease-molecule associations, while machine learning, statistics, and information retrieval were utilized in smaller and moderate sized studies. Multiscale computations comprising shared function, network topology, and phenotypes were performed exclusively on proteins. CONCLUSION This review highlighted the growing methods for identifying the molecular mechanisms underpinning comorbidities that leverage multiscale molecular information and patterns from electronic health records. The survey unveiled that intergenic polymorphisms have been overlooked for similarity imputation compared to their intragenic counterparts, offering new opportunities to bridge the mechanistic and similarity gaps of comorbidity.
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Affiliation(s)
| | | | | | | | | | | | - Y A Lussier
- Dr. Yves A. Lussier, The University of Arizona, Bio5 Building, 1657 East Helen Street, Tucson, AZ 85721, USA, Fax: +1 520 626 4824, E-Mail:
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Chang D, Gao F, Slavney A, Ma L, Waldman YY, Sams AJ, Billing-Ross P, Madar A, Spritz R, Keinan A. Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases. PLoS One 2014; 9:e113684. [PMID: 25479423 PMCID: PMC4257614 DOI: 10.1371/journal.pone.0113684] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 10/30/2014] [Indexed: 12/12/2022] Open
Abstract
Many complex human diseases are highly sexually dimorphic, suggesting a potential contribution of the X chromosome to disease risk. However, the X chromosome has been neglected or incorrectly analyzed in most genome-wide association studies (GWAS). We present tailored analytical methods and software that facilitate X-wide association studies (XWAS), which we further applied to reanalyze data from 16 GWAS of different autoimmune and related diseases (AID). We associated several X-linked genes with disease risk, among which (1) ARHGEF6 is associated with Crohn's disease and replicated in a study of ulcerative colitis, another inflammatory bowel disease (IBD). Indeed, ARHGEF6 interacts with a gastric bacterium that has been implicated in IBD. (2) CENPI is associated with three different AID, which is compelling in light of known associations with AID of autosomal genes encoding centromere proteins, as well as established autosomal evidence of pleiotropy between autoimmune diseases. (3) We replicated a previous association of FOXP3, a transcription factor that regulates T-cell development and function, with vitiligo; and (4) we discovered that C1GALT1C1 exhibits sex-specific effect on disease risk in both IBDs. These and other X-linked genes that we associated with AID tend to be highly expressed in tissues related to immune response, participate in major immune pathways, and display differential gene expression between males and females. Combined, the results demonstrate the importance of the X chromosome in autoimmunity, reveal the potential of extensive XWAS, even based on existing data, and provide the tools and incentive to properly include the X chromosome in future studies.
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Affiliation(s)
- Diana Chang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, United States of America
| | - Feng Gao
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Andrea Slavney
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Graduate Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York, United States of America
| | - Li Ma
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland, United States of America
| | - Yedael Y. Waldman
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Aaron J. Sams
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Paul Billing-Ross
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Graduate Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York, United States of America
| | - Aviv Madar
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Richard Spritz
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Alon Keinan
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, United States of America
- Graduate Field of Genetics, Genomics and Development, Cornell University, Ithaca, New York, United States of America
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